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Information Visualization Course

Information Visualization Course. Information Visualization. Prof. Anselm Spoerri aspoerri@scils.rutgers.edu. Course Goals. Information Visualization = Emerging Field Provide Thorough Introduction Information Visualization aims To use human perceptual capabilities

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Information Visualization Course

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  1. Information Visualization Course Information Visualization Prof. Anselm Spoerri aspoerri@scils.rutgers.edu

  2. Course Goals • Information Visualization = Emerging Field Provide Thorough Introduction • Information Visualization aims To use human perceptual capabilities To gain insights into large and abstract data sets that are difficult to extract using standard query languages • Abstract and Large Data Sets Symbolic Tabular Networked Hierarchical Textual information

  3. Approach • Foundation in Human Visual Perception How it relates to creating effective information visualizations. • Understand Key Design Principles for Creating Information Visualizations • Study Major Information Visualization Tools • Evaluate Information Visualizations Tools MetaCrystal – Visual Retrieval Interface • Design New, Innovative Visualizations

  4. Approach (cont.) • 1 Human Visual Perception + Human Computer Interaction “Information Visualization: Perception for Design”by Colin Ware, Morgan Kaufmann, 2000 • 2 Key Papers in Information Visualizations “Modern Information Retrieval - Chapter 10: User Interfaces and Visualization” by Marti Hearst Key Papers from "Readings in Information Visualization: Using Visualization to Think" and other sources – available electronically • 3 Develop / Evaluate searchCrystal • 4 Term Projects Review + Analyze Visualizations Tools Evaluate Visualizations Tool Design Visualizations Prototype

  5. Approach (cont.) • Class = Graduate Seminar Literature Review based on Seed Articles Discussion of Assigned Readings Viewing of Videos Hands-on Experience of Tools Evaluation of Tools

  6. Gameplan • Course Websitehttp://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Home.htm • Requirement • “Modern Information Retrieval - Chapter 10: User Interfaces and Visualization” by Marti Hearsthttp://www.sims.berkeley.edu/~hearst/irbook/10/node1.html • Key Papers from "Readings in Information Visualization: Using Visualization to Think" and other sources – available electronically • Grading • Short Reviews of Assigned Papers (20%) • Group Evaluation of InfoVis Tool (20%) • Research Topic & Class Presentation (20%) • Final Project (40%)

  7. Gameplan (cont.) • Schedule • Link to Lecture Slides • http://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Schedule.htm • Lecture Slideshttp://scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/Lectures/Lectures.htm • Lecture Slidesposted before lecture • Slides Handout available for download & print-out • Open in Powerpoint • File > Print … • “Print what” = “Handout” • Select “2 slides” per page

  8. Term Projects • Review + Analyze Visualization Tools • Describe topic and approach you plan to take • Length: 20 to 25 pages • Evaluate Visualization Tool • Describe tool you want to evaluate as well as why and how • Describe and motivate evaluation design • Conduct evaluation with 3 to 5 people • Report on evaluation results • Potential Tools to evaluate: Star Trees, Personal Brain, … your suggestion • Design Visualization Prototype • Describe data domain, motivate your choice and describe your programming expertise • Describe design approach • Develop prototype • Present prototype • Will provide more specific instructions next week. • Send instructor email with short description of your current project idea by Week 7

  9. Your Guide • Anselm Spoerri • Computer Vision • Filmmaker – IMAGO • Click on the center image to play video • Information Visualization – InfoCrystal searchCrystal • Media Sharing – Souvenir • In Action Examples: click twice on digital ink or play button • Rutgers Website

  10. Wish • Help me • Develop searchCrystal • into • Effective • Tested • Visual Retrieval Tool • How? Testing of searchCrystal and Provide Feedback Testing of Related Tools

  11. Goal of Information Visualization • Use human perceptual capabilitiesto gain insightsintolarge data setsthat aredifficult to extractusing standard query languages • Exploratory Visualization • Look for structure, patterns, trends, anomalies, relationships • Provide a qualitative overview of large, complex data sets • Assist in identifying region(s) of interest and appropriate parameters for more focussed quantitative analysis • Shneiderman's Mantra: • Overview first, zoom and filter, then details-on-demand • Overview first, zoom and filter, then details-on-demand • Overview first, zoom and filter, then details-on-demand

  12. Information Visualization - Problem Statement • Scientific Visualization • Show abstractions, but based on physical space • Information Visualization • Information does not have any obvious spatial mapping • Fundamental Problem How to map non–spatial abstractions into effective visual form? • Goal Use of computer-supported, interactive, visual representations of abstract data to amplify cognition

  13. How Information Visualization Amplifies Cognition • Increased Resources  • Parallel perceptual processing  • Offload work from cognitive to perceptual system  • Reduced Search • High data density  • Greater access speed • Enhanced Recognition of Patterns  • Recognition instead of Recall  • Abstraction and Aggregation   • Perceptual Interference • Perceptual Monitoring  • Color or motion coding to create pop out effect • Interactive Medium  

  14. Information Visualization – Key Design Principles • Information Visualization = Emerging Field • Key Principles • Abstraction • Overview  Zoom+Filter  Details-on-demand • Direct Manipulation • Dynamic Queries • Immediate Feedback • Linked Displays • Linking + Brushing • Provide Focus + Context • Animate Transitions and Change of Focus • Output is Input • Increase Information Density

  15. Position More Accurate Length Angle Slope Area Volume Color Density Less Accurate Mapping Data to Display Variables • Position (2) • Orientation (1) • Size (spatial frequency) • Motion (2)++ • Blinking? • Color (3) • Accuracy Ranking for Quantitative Perceptual Tasks

  16. Information Visualization – “Toolbox” Perceptual Coding Interaction Information Density

  17. Interaction – Timings + Mappings • Interaction Responsiveness “0.1” second • Perception of Motion • Perception of Cause & Effect “1.0” second  “Unprepared response” “10” seconds • Pace of routine cognitive task • Mapping Data to Visual Form • Variables Mapped to “Visual Display” • Variables Mapped to “Controls” • “Visual Display” and “Controls” Linked

  18. Spatial vs. Abstract Data • "Spatial" Data • Has inherent 1-, 2- or 3-D geometry • MRI: density, with 3 spatial attributes, 3-D grid connectivity • CAD: 3 spatial attributes with edge/polygon connections, surface properties • Abstract, N-dimensional Data • Challenge of creating intuitive mapping • Chernoff Faces • Software Visualization: SeeSoft • Scatterplot and Dimensional Stacking • Parallel Coordinates and Table Lens • Hierarchies: Treemaps, Brain,Hyperbolic Tree • Boolean Query: Filter-Flow, InfoCrystal

  19. "Spatial" Data DisplaysIBM Data Explorer

  20. "Spatial" Data DisplaysIBM Data Explorer

  21. Abstract, N-Dimensional Data Displays • Chernoff Faces

  22. Abstract, N-Dimensional Data Displays • Scatterplot and Dimensional Stacking

  23. Abstract, N-Dimensional Data Displays • Parallel CoordinatesbyIsenberg (IBM)

  24. Abstract, N-Dimensional Data Displays • Software Visualization - SeeSoft Line = single line of source code and its length Color = different properties

  25. Treemap Hyperbolic Tree Traditional Botanical SunTree ConeTree Abstract  Hierarchical Information – Preview

  26. Treemaps – Shading & Color Coding

  27. Abstract  Hierarchical Information • Hierarchy Visualization - ConeTree

  28. Abstract  Hierarchical Information • Hyperbolic Tree  Demo • http://www.inxight.com/products/sdks/st/

  29. Table Lens – Demo • http://www.inxight.com/products/sdks/tl/ • Select Ameritrade TableLens • See if you can find • The Largest loss value for a fund for this QuarterHint: greater than -30.0 • The Largest ten year gain. Hint: greater than +900.0 • The Largest five year gain. Hint: a Lipper fund • See what else you can find...

  30. Kartoo Grokker MetaCrystal MSN Lycos AltaVista Abstract  Text – MetaSearch Previews

  31. Abstract  Text • Document Visualization - ThemeView

  32. From Theory to Commercial Products • Xerox PARC • Inxight • University of Maryland & Shneiderman • Spotfire • AT&T Bell Labs & Lucent • Visual Insights • TheBrain

  33. Information Visualization – Key Design Principles – Recap • Direct Manipulation • Immediate Feedback • Linked Displays • Dynamic Queries • Tight Coupling Output  Input • Overview  Zoom+Filter  Details-on-demand • Provide Context + Focus • Animate Transitions • Increase Information Density

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